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This study looks at how basic research capabilities develop within enterprise clusters, focusing on the complex and adaptive nature of these systems. It builds a conceptual model using systems theory and applies information entropy to measure how much these capabilities have emerged. This study introduces an innovative application of information entropy to model and quantify the emergence of research capabilities within enterprise clusters, offering a novel framework for assessing research development. To dive deeper, China Pingmei Shenma Group (Henan, China) was used as a case study. A case study approach was used to gather empirical data. This case—focused on a state-owned enterprise cluster in China's coal-based energy and chemical industries—highlights the key factors that influence research capability growth. These factors include support from external systems, how internal resources are used, and their renewal over time. From 2017 to 2022, the study tracked how the organization of research capabilities evolved over time by tracking changes in entropy, revealing the process of research development driven by both internal and external forces. The methodology involves measuring system entropy to evaluate the degree of orderliness and innovation performance, incorporating entropy generation and exchange metrics, which allows for a more precise understanding of system emergence and complexity. The interactions within the system, such as knowledge exchange, research collaboration, and external input from government subsidies or tax incentives, are modeled to track how they influence the system's overall entropy. This study finds that the ability of an enterprise cluster to bring in external resources and reduce internal inefficiencies is critical for enhancing research capabilities. This model can help policymakers and enterprises in strategic decision-making, particularly in industries undergoing technological transformation. This framework also provides practical insights for improving research collaboration and innovation in enterprise clusters, especially in rapidly evolving industries like energy and chemicals. [ABSTRACT FROM AUTHOR]

This study looks at how basic research capabilities develop within enterprise clusters, focusing on the complex and adaptive nature of these systems. It builds a conceptual model using systems theory and applies information entropy to measure how much these capabilities have emerged. This study introduces an innovative application of information entropy to model and quantify the emergence of research capabilities within enterprise clusters, offering a novel framework for assessing research development. To dive deeper, China Pingmei Shenma Group (Henan, China) was used as a case study. A case study approach was used to gather empirical data. This case—focused on a state-owned enterprise cluster in China’s coal-based energy and chemical industries—highlights the key factors that influence research capability growth. These factors include support from external systems, how internal resources are used, and their renewal over time. From 2017 to 2022, the study tracked how the organization of research capabilities evolved over time by tracking changes in entropy, revealing the process of research development driven by both internal and external forces. The methodology involves measuring system entropy to evaluate the degree of orderliness and innovation performance, incorporating entropy generation and exchange metrics, which allows for a more precise understanding of system emergence and complexity. The interactions within the system, such as knowledge exchange, research collaboration, and external input from government subsidies or tax incentives, are modeled to track how they influence the system’s overall entropy. This study finds that the ability of an enterprise cluster to bring in external resources and reduce internal inefficiencies is critical for enhancing research capabilities. This model can help policymakers and enterprises in strategic decision-making, particularly in industries undergoing technological transformation. This framework also provides practical insights for improving research collaboration and innovation in enterprise clusters, especially in rapidly evolving industries like energy and chemicals.